Applied Econometrics: A Practical Guide (Routledge Advanced Texts in Economics and Finance)

313 Pages·2019·8.796 MB·English

by  Chung-ki Min

APPLIED ECONOMETRICS
Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book
on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions,
this book is perfect for beginners and promises simplicity and practicality to the understanding of
econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and
moves forth to simple and multiple regression models. It also includes advanced topics:
• Endogeneity and Two-stage Least Squares
• Simultaneous Equations Models
• Panel Data Models
• Qualitative and Limited Dependent Variable Models
• Vector Autoregressive (VAR) Models
• Autocorrelation and ARCH/GARCH Models
• Unit Root and Cointegration
The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating
and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation
in the fundamentals of econometrics. In addition, this book includes excerpts from relevant articles
published in top-tier academic journals. This integration of published articles helps the readers to
understand how econometric models are applied to real-world use cases.
Chung-ki Min is Professor of Economics at Hankuk University of Foreign Studies, Seoul, South
Korea.
Routledge Advanced Texts in Economics and Finance
Finance in Asia
Institutions, Regulation and Policy
Qiao Liu, Paul Lejot and Douglas Arner
Behavioral Economics, Second Edition
Edward Cartwright
Understanding Financial Risk Management
Angelo Corelli
Empirical Development Economics
Måns Söderbom and Francis Teal with Markus Eberhardt, Simon Quinn and Andrew Zeitlin
Strategic Entrepreneurial Finance
From Value Creation to Realization
Darek Klonowski
Computational Economics
A Concise Introduction
Oscar Afonso and Paulo Vasconcelos
Regional Economics, Second Edition
Roberta Capello
Game Theory and Exercises
Gisèle Umbhauer
Innovation and Technology
Business and Economics Approaches
Nikos Vernardakis
Behavioral Economics, Third Edition
Edward Cartwright
Applied Econometrics
A Practical Guide
Chung-ki Min
For more information about this series, please visit www.routledge.com/Routledge-Advanced-Texts-
in-Economics-and-Finance/book-series/SE0757
APPLIED
ECONOMETRICS
A Practical Guide
Chung-ki Min
First published 2019
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
52 Vanderbilt Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2019 Chung-ki Min
The right of Chung-ki Min to be identified as author of this work has been asserted by
him in accordance with sections 77 and 78 of the Copyright, Designs and Patents
Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced or utilised in
any form or by any electronic, mechanical, or other means, now known or hereafter
invented, including photocopying and recording, or in any information storage or
retrieval system, without permission in writing from the publishers.
Trademark notice: Product or corporate names may be trademarks or registered
trademarks, and are used only for identification and explanation without intent to
infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
Names: Min, Chung Ki, author.
Title: Applied econometrics : a practical guide / by Chung-ki Min.
Description: Abingdon, Oxon ; New York, NY : Routledge, 2019. | Series: Routledge
advanced texts in economics and finance ; 31 | Includes bibliographical references
and index.
Identifiers: LCCN 2018049763 | ISBN 9780367110321 (hardback) |
ISBN 9780367110338 (pbk.) | ISBN 9780429024429 (ebook)
Subjects: LCSH: Econometrics.
Classification: LCC HB139 .M555 2019 | DDC 330.01/5195—dc23
LC record available at https://lccn.loc.gov/2018049763
ISBN: 978-0-367-11032-1 (hbk)
ISBN: 978-0-367-11033-8 (pbk)
ISBN: 978-0-429-02442-9 (ebk)
Typeset in Times New Roman
by Apex CoVantage, LLC
Vist the eResources: http://routledgetextbooks.com/textbooks/instructor_downloads/
CONTENTS
List of figures xi
List of tables xii
Preface xiii
Acknowledgments xv
1 Review of estimation and hypothesis tests 1
1.1 The problem 1
1.2 Population and sample 1
1.3 Hypotheses 2
1.4 Test statistic and its sampling distribution 2
1.5 Type I and Type II errors 4
1.6 Significance level 4
1.7 p-value 4
1.8 Powerful tests 5
1.9 Properties of estimators 8
1.10 Summary 9
Review questions 10
2 Simple linear regression models 12
2.1 Introduction 12
2.1.1 A hypothetical example 12
2.1.2 Population regression line 13
2.1.3 Stochastic specification for individuals 14
2.2 Ordinary least squares estimation 14
2.3 Coefficient of determination (R2) 17
2.3.1 Definition and interpretation of R2 17
2.3.2 Application of R2: Morck, Yeung and Yu (2000) 18
2.3.3 Application of R2: Dechow (1994) 19
2.4 Hypothesis test 20
2.4.1 Testing H :β =0 vs. H :β ≠0 20
0 1 1 1
2.4.2 Testing H :β =c vs. H :β ≠c (c is a constant) 22
0 1 1 1
2.5 The model 22
2.5.1 Key assumptions 22
2.5.2 Gauss-Markov Theorem 26
2.5.3 Consistency of the OLS estimators 26
2.5.4 Remarks on model specification 27
vi Contents
2.6 Functional forms 27
2.6.1 Log-log linear models 28
2.6.2 Log-linear models 30
2.7 Effects of changing measurement units and levels 31
2.7.1 Changes of measurement units 31
2.7.2 Changes in the levels 33
2.8 Summary 33
Review questions 34
References 38
Appendix 2 How to use EViews, SAS and R 39
3 Multiple linear regression models 43
3.1 The basic model 43
3.2 Ordinary least squares estimation 45
3.2.1 Obtaining the OLS estimates 45
3.2.2 Interpretation of regression coefficients 46
3.3 Estimation bias due to correlated-omitted variables 47
3.4 R2 and the adjusted R2 48
3.4.1 Definition and interpretation of R2 48
3.4.2 Adjusted R2 48
3.5 Hypothesis test 49
3.6 Model selection 50
3.6.1 General-to-simple approach 50
3.6.2 A comment on hypothesis testing 52
3.6.3 Guidelines for model selection 53
3.7 Applications 53
3.7.1 Mitton (2002) 53
3.7.2 McAlister, Srinivasan and Kim (2007) 55
3.7.3 Collins, Pincus and Xie (1999) 56
3.7.4 Angrist and Pixchke (2009, pp. 64–68) 57
3.8 Summary 59
Review questions 59
References 64
Appendix 3A Hypothesis test using EViews and SAS 65
Appendix 3B Geometric interpretation of the OLS regression equation 67
4 Dummy explanatory variables 69
4.1 Dummy variables for different intercepts 69
4.1.1 When there are two categories 69
4.1.2 When there are more than two categories 72
4.1.3 Interpretation when the dependent variable is in logarithm 72
4.1.4 Application: Mitton (2002) 73
4.1.5 Application: Hakes and Sauer (2006) 74
4.2 Dummy variables for different slopes 77
4.2.1 Use of a cross product with a dummy variable 77
4.2.2 Application: Basu (1997) 79
4.3 Structural stability of regression models 80
4.3.1 Test by splitting the sample (Chow test) 80
4.3.2 Test using dummy variables 80
Contents vii
4.4 Piecewise linear regression models 81
4.4.1 Using dummy variables 81
4.4.2 Using quantitative variables only 81
4.4.3 Morck, Shleifer and Vishny (1988) 82
4.5 Summary 83
Review questions 83
References 86
Appendix 4 Dummy variables in EViews and SAS 87
5 More on multiple regression analysis 89
5.1 Multicollinearity 89
5.1.1 Consequences of multicollinearity 91
5.1.2 Solutions 91
5.2 Heteroscedasticity 92
5.2.1 Consequences of heteroscedasticity 92
5.2.2 Testing for heteroscedasticity 92
5.2.3 Application: Mitton (2002) 93
5.3 More on functional form 94
5.3.1 Quadratic function 94
5.3.2 Interaction terms 94
5.4 Applications 96
5.4.1 Bharadwaj, Tuli and Bonfrer (2011) 96
5.4.2 Ghosh and Moon (2005) 98
5.4.3 Arora and Vamvakidis (2005) 99
5.5 Summary 100
Review questions 100
References 105
Appendix 5 Testing and correcting for heteroscedasticity 106
6 Endogeneity and two-stage least squares estimation 109
6.1 Measurement errors 110
6.1.1 Measurement errors in the dependent variable 111
6.1.2 Measurement errors in an explanatory variable 111
6.2 Specification errors 113
6.2.1 Omitted variables 113
6.2.2 Inclusion of irrelevant variables 114
6.2.3 A guideline for model selection 114
6.3 Two-stage least squares estimation 115
6.4 Generalized method of moments (GMM) 117
6.4.1 GMM vs. 2SLS 118
6.5 Tests for endogeneity 118
6.5.1 Ramsey (1969) test 118
6.5.2 Hausman (1978) test 118
6.6 Applications 119
6.6.1 Dechow, Sloan and Sweeney (1995) 119
6.6.2 Beaver, Lambert and Ryan (1987) 121
6.6.3 Himmelberg and Petersen (1994) 122
6.7 Summary 122
Review questions 123
viii Contents
References 127
Appendix 6A Estimation of 2SLS and GMM using EViews and SAS 129
Appendix 6B Hausman test for endogeneity using EViews and SAS 132
7 Models for panel data 135
7.1 One big regression 135
7.2 Fixed effects model 136
7.2.1 Using time dummies (for b) 136
t
7.2.2 Using cross-section dummies (for a) 137
i
7.2.3 Applying transformations 137
7.3 Applications 139
7.3.1 Cornwell and Trumbull (1994) 139
7.3.2 Blackburn and Neumark (1992) 141
7.3.3 Garin-Munoz (2006) 142
7.3.4 Tuli, Bharadwaj and Kohli (2010) 142
7.4 Random effects 145
7.5 Fixed vs. random effects models 147
7.6 Summary 147
Review questions 148
References 151
Appendix 7A Controlling for fixed effects using EViews and SAS 153
Appendix 7B Is it always possible to control for unit-specific effects? 155
8 Simultaneous equations models 157
8.1 Model description 157
8.2 Estimation methods 158
8.2.1 Two-stage least squares (2SLS) 158
8.2.2 Three-stage least squares (3SLS) 159
8.2.3 Generalized method of moments (GMM) 160
8.2.4 Full-information maximum likelihood (FIML) 160
8.3 Identification problem 160
8.4 Applications 162
8.4.1 Cornwell and Trumbull (1994) 162
8.4.2 Beaver, McAnally and Stinson (1997) 163
8.4.3 Barton (2001) 165
8.4.4 Datta and Agarwal (2004) 165
8.5 Summary 167
Review questions 167
References 169
Appendix 8 Estimation of simultaneous equations models using EViews and SAS 171
9 Vector autoregressive (VAR) models 173
9.1 VAR models 173
9.2 Estimation of VAR models 174
9.3 Granger-causality test 175
9.4 Forecasting 178
9.5 Impulse-response analysis 179
9.6 Variance decomposition analysis 181
9.7 Applications 183
9.7.1 Stock and Watson (2001) 183
Contents ix
9.7.2 Zhang, Fan, Tsai and Wei (2008) 186
9.7.3 Trusov, Bucklin and Pausels (2009) 187
9.8 Summary 191
Review questions 191
References 194
Appendix 9 Estimation and analysis of VAR models using SAS 195
10 Autocorrelation and ARCH/GARCH 203
10.1 Autocorrelation 203
10.1.1 Consequences of autocorrelation 203
10.1.2 Test for autocorrelation 206
10.1.3 Estimation of autocorrelation 208
10.2 ARCH-type models 208
10.2.1 ARCH model 209
10.2.2 GARCH (Generalized ARCH) model 212
10.2.3 TGARCH (Threshold GARCH) model 214
10.2.4 EGARCH (Exponential GARCH) model 214
10.2.5 GARCH-M model 215
10.3 Applications 215
10.3.1 Wang, Salin and Leatham (2002) 215
10.3.2 Zhang, Fan, Tsai and Wei (2008) 216
10.3.3 Value at Risk (VaR) 218
10.4 Summary 219
Review questions 220
References 222
Appendix 10A Test and estimation of autocorrelation using EViews and SAS 223
Appendix 10B Test and estimation of ARCH/GARCH models using SAS 229
11 Unit root, cointegration and error correction model 230
11.1 Spurious regression 230
11.2 Stationary and nonstationary time series 232
11.3 Deterministic and stochastic trends 233
11.4 Unit root tests 234
11.4.1 Dickey-Fuller (DF) test 234
11.4.2 Augmented Dickey-Fuller (ADF) test 235
11.4.3 Example: unit root test using EViews 235
11.5 Cointegration 237
11.5.1 Tests for cointegration 237
11.5.2 Vector error correction models (VECMs) 237
11.5.3 Example: test and estimation of cointegration using EViews 238
11.6 Applications 242
11.6.1 Stock and Watson (1988) 242
11.6.2 Baillie and Selover (1987) 243
11.6.3 Granger (1988) 243
11.6.4 Dritsakis (2004) 245
11.6.5 Ghosh (1993) 247
11.7 Summary 247
Review questions 247
References 250